class_docstr: You can also import this e.g from .test_modeling_markuplm import MarkupLMModelTester
code: def create_and_check_model(
      self,
      config,
      input_ids,
      xpath_tags_seq,
      xpath_subs_seq,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
  ):
      model = MarkupLMModel(config=config)
      model.to(torch_device)
      model.eval()
      print(
          "Configs:", model.config.tag_pad_id, model.config.subs_pad_id
      )
      result = model(
          input_ids,
          attention_mask=input_mask,
          token_type_ids=token_type_ids,
      )
      result = model(input_ids, token_type_ids=token_type_ids)
      result = model(input_ids)
      self.parent.assertEqual(
          result.last_hidden_state.shape,
          (self.batch_size, self.seq_length, self.hidden_size),
      )
      self.parent.assertEqual(
          result.pooler_output.shape,
          (self.batch_size, self.hidden_size),
      )
  
imports: import unittest
  from transformers import MarkupLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer
label_desc: Use image node_id 4 for calling the MarkupLMModelTester obj's underlying member method code with example usage: obj.create_and_check_model(config, input_ids, xpath_tags_seq, xpath_subs_seq, token_type_ids, input_mask, sequence_labels, token_labels) without return types